An Experimental Study on Fundamental Frequency Detection in Reverberated Speech with Pre-trained Recurrent Neural Networks-Reference-Cited by-同舟云学术

An Experimental Study on Fundamental Frequency Detection in Reverberated Speech with Pre-trained Recurrent Neural Networks

Published:2020 Issue: Volume: Page:355-368
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Alfaro-Picado Andrei^ORCID,Solís-Cerdas Stacy^ORCID,Coto-Jiménez Marvin^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-41005-6_24

Reference42 articles.

1. Abdel-Hamid, O., Mohamed, A.R., Jiang, H., Penn, G.: Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4277–4280. IEEE (2012)

2. Baek, J., Cho, S.: Bankruptcy prediction for credit risk using an auto-associative neural network in Korean firms. In: 2003 Proceedings of the IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. pp. 25–29. IEEE (2003)

3. Bagchi, D., Mandel, M.I., Wang, Z., He, Y., Plummer, A., Fosler-Lussier, E.: Combining spectral feature mapping and multi-channel model-based source separation for noise-robust automatic speech recognition. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 496–503. IEEE (2015)

4. Bengio, Y., Frasconi, P., Simard, P.: The problem of learning long-term dependencies in recurrent networks. In: IEEE International Conference on Neural Networks, pp. 1183–1188. IEEE (1993)

5. Coto-Jiménez, M.: Pre-training long short-term memory neural networks for efficient regression in artificial speech postfiltering. In: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 1–7. IEEE (2018)